Side by side
forestci vs rsimsum
Both stand on statistical analysis software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Free plan APILinux
forestci adds uncertainty calculations directly to supported scikit-learn random-forest estimators. Its calibration option and tree-count requirements require judgment when interpreting results.
Visit forestci Free plan MacWindows
rsimsum provides a broad set of simulation-study measures, default Monte Carlo uncertainty summaries, and varied ggplot2 visualizations at no cost. Its main functional constraint is that simsum processes one estimand at a time; multisimsum is available for multiple estimands.
Visit rsimsumThe facts
| forestci | rsimsum | |
|---|---|---|
| Free plan | Yes | Yes |
| Open source | Not published | Not published |
| Runs on | API, Linux | Mac, Windows |
| Lowest paid plan | Free or on request | Not published |
| Plans published | 1 | None |
| Maker | Not published | rsimsum |
| Website | contrib.scikit-learn.org | ellessenne.github.io |
| On statistical analysis software | #42 of 106 | #46 of 106 |
forestci: what it does well
- Free MIT-licensed package
- Works with regression and classification forests
- Optional Empirical Bayes calibration
- Supports scikit-learn 1.0 through 1.9
- Includes regression and classification examples
What to know first
- Requires numpy, scipy, and scikit-learn
- Confidence intervals need many trees
- Calibration may worsen empirical agreement
- Linux API package
rsimsum: what it does well
- Free R package under GPL (>= 3)
- Monte Carlo errors and intervals by default
- Many simulation summary measures
- Extensive ggplot2 visualizations
- CRAN and GitHub installation options
What to know first
- simsum handles one estimand at a time
- Requires an R environment
- Desktop binaries listed for Windows and macOS
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